Sr. Machine Learning Engineer (Causality)

Company Description

Our Mission

At Palo Alto Networks® everything starts and ends with our mission:

Being the cybersecurity partner of choice, protecting our digital way of life.

Our vision is a world where each day is safer and more secure than the one before. We are a company built on the foundation of challenging and disrupting the way things are done, and we’re looking for innovators who are as committed to shaping the future of cybersecurity as we are.

FLEXWORK is an employee-centric reimagining of how we work. We built FLEXWORK based on employee feedback – it is about flexibility, trust, and choice whenever possible. It’s been a journey of disruption that has yielded the best of our values. We offer as much flexibility as possible, and choices that enable you to be most productive, including benefits that meet your needs and learning opportunities that you feel passionate about.

Our Approach to Work

At Palo Alto Networks, we believe in the power of collaboration and value in-person interactions. This is why our employees generally work from the office three days per week, leaving two days for choice and flexibility to work where you feel most effective. This setup fosters casual conversations, problem-solving, and trusted relationships. While details may evolve, our goal is to create an environment where innovation thrives, with office-based teams coming together three days a week to collaborate and thrive, together!

Job Description

Your Career

You will be able to build a career applying Machine Learning and AI to different challenges around Firewalls and Network Security Operations in general. 

From Timeseries techniques like Anomaly Detection and forecasting, passing through Causality techniques for Root Cause Analysis, all the way to recent advances like Generative AI, you will never be out of exciting challenges.

You will help to create AI solutions on top of one of the biggest data lakes with exabytes of data and hundreds of thousands of devices constantly sending telemetry. 

At Palo Alto Networks, Sr. Machine Learning Engineers are:

• Experts in concrete areas of statistics, machine learning, or AI in general. 
• Committed professionals who deliver ML features that design have solid evidence and support given by strong data analysis practices and outcomes.
• Analytical engineers that are always making decisions and designs based on data.
• Persons who are curious about the latest technologies, always learning new techniques, and highly driven by a deep understanding of problems.

Your Impact

• You will work in a fast-paced team to create and deliver new features in a speedboat product that many customers use daily to ensure their network operation is healthy and secure.
• You will be part of a team using ML and AI to redefine the way Network Security operations are done.
• You will help to design and develop AI/ML frameworks that allow us to apply algorithms at the scale of hundreds of thousands of devices reporting thousands of metrics on a minute basis.
• You will collaborate in the design of user experiences that allow users to consume deep and complex insights in an easy and manageable way.
• You will play a key role in the design, development, and implementation of causal inference (and causal discovery) techniques across the product.

Qualifications

Your Experience

• Formal background and experience in both causal inference methods and causal discovery approaches. Including classical techniques like Propensity Score, matching, difference in difference, and so on, and also new techniques like double machine learning or Potential Outcomes.
• M.S. or Ph.D degree in Econometrics, Computer Science, Mathematics, Statistics or related field.
• 2+ years of industry experience (or equivalent education + experience) in Machine Learning techniques and data analysis. Experience with Timeseries, Causality, and NLP is a plus.
• 8+ experience in design, algorithms, and data structures - Expertise with one or more of the following languages is a must - Java, Python.
• Proven capacity to go beyond ML algorithms and models by contributing to the design and implementation of services and pipelines that expose ML artifacts.
• Experience with deep learning frameworks such as PyTorch and TensorFlow, Huggingface Transformers, or related is a plus.
• Hands-on experience with causal inference libraries and frameworks.
• Capacity to become a self-driven individual contributor and an excellent team player when the teams and the business require it.

Additional Information

The Team

We are on a mission to build the